Thread-local storage (TLS) in C++ can introduce significant performance overhead, even when unused. The author benchmarks various TLS access methods, demonstrating that even seemingly simple zero-initialized thread-local variables incur a cost, especially on Windows. This overhead stems from the runtime needing to manage per-thread data structures, including lazy initialization and destruction. While the performance impact might be negligible in many applications, it can become noticeable in highly concurrent, performance-sensitive scenarios, particularly with a large number of threads. The author explores techniques to mitigate this overhead, such as using compile-time initialization or avoiding TLS altogether if practical. By understanding the costs associated with TLS, developers can make informed decisions about its usage and optimize their multithreaded C++ applications for better performance.
The blog post "0+0 > 0: C++ thread-local storage performance" by Yosef Kreinin explores the performance implications of using thread-local storage (TLS) in C++. Kreinin begins by establishing the context that accessing thread-local variables can introduce performance overhead, potentially negating the benefits of multithreading. He sets out to investigate the extent of this overhead and identify the contributing factors.
The investigation starts with a simple benchmark that measures the time taken to perform a trivial arithmetic operation (0+0) within a loop, both with and without declaring a thread-local variable. Surprisingly, the benchmark reveals that the version with the thread-local variable is significantly slower, even though the variable is never accessed. This indicates that the mere presence of a thread-local variable introduces overhead.
Kreinin then delves into the potential reasons for this performance degradation. He explains that TLS is typically implemented using a hidden global data structure accessed indirectly through thread-local storage pointers. Each thread maintains its own pointer to its respective slot in this structure. The access to a thread-local variable involves retrieving the thread-local storage pointer, which can be a relatively expensive operation depending on the platform and implementation. Furthermore, the added complexity can disrupt compiler optimizations, hindering performance.
The post examines several scenarios and their corresponding assembly code to demonstrate how thread-local variables impact performance. These scenarios include cases where the variable is initialized with a constant, initialized with a non-constant expression, and cases where the variable is accessed or not accessed within the loop. The analysis of the generated assembly code illuminates the underlying mechanisms responsible for the observed performance differences. It highlights the additional instructions required for thread-local variable access, compared to regular global or local variables.
Kreinin further investigates how different compilers and operating systems handle TLS. He observes variations in performance across different platforms, suggesting that the overhead associated with thread-local variables is not uniform. This emphasizes the importance of understanding the specific implementation details when working with TLS.
The post then explores strategies for mitigating the performance impact of thread-local variables. One such strategy involves reducing the number of thread-local variables by grouping related variables into a structure. This technique minimizes the number of indirect accesses required, potentially improving performance. Another approach involves caching the value of a thread-local variable in a local variable within a tight loop, thereby avoiding repeated access to the TLS mechanism.
The blog post concludes by summarizing the findings and emphasizing the importance of considering the performance implications of thread-local storage when designing multithreaded C++ applications. It advises developers to be mindful of the potential overhead and to employ appropriate optimization techniques when necessary. The key takeaway is that while thread-local storage provides a valuable mechanism for managing thread-specific data, its usage should be carefully considered in performance-critical sections of code.
Summary of Comments ( 10 )
https://news.ycombinator.com/item?id=43077675
The Hacker News comments discuss the surprising performance cost of thread-local storage (TLS) in C++, particularly its impact on seemingly unrelated code. Several commenters highlight the overhead introduced by the TLS lookups, even when the TLS variables aren't directly used in a particular code path. The most compelling comments delve into the underlying reasons for this, citing issues like increased register pressure due to the extra variables needing to be tracked, and the difficulty compilers have in optimizing around TLS access. Some point out that the benchmark's reliance on
rdtscfor timing might be flawed, while others offer alternative benchmarking strategies. The performance impact is acknowledged to be architecture-dependent, with some suggesting mitigations like using compile-time initialization or alternative threading models if TLS performance is critical. A few commenters also mention similar performance issues they've encountered with TLS in other languages, suggesting it's not a C++-specific problem.The Hacker News post titled "0+0 > 0: C++ thread-local storage performance," linking to an article about C++ thread-local storage performance, has a moderate number of comments discussing various aspects of the topic.
Several commenters discuss the complexities and nuances of thread-local storage (TLS) implementation across different compilers and platforms. One commenter points out the variability in performance characteristics of TLS, noting how different compilers (like GCC and Clang) and operating systems might handle TLS access differently, impacting performance. This commenter also highlights how the use of dynamic libraries can further complicate the situation, leading to potential performance hits if TLS isn't implemented optimally within the dynamic loading process.
Another commenter delves into the specifics of how TLS is handled on Windows, mentioning the use of "Thread Local Storage (TLS) callbacks," which are functions executed upon thread creation or destruction that manage the TLS data. This introduces overhead, especially in scenarios with frequent thread creation and destruction. The commenter contrasts this with the __thread keyword (supported by GCC and Clang), which is often faster but less portable.
One commenter mentions the difficulties in measuring the performance of TLS accurately, emphasizing the importance of factors such as CPU caching and benchmarking methodology. They also point out the impact that the surrounding code and its interaction with the TLS access can have on overall performance.
The discussion also touches upon the performance implications of different TLS access patterns. One commenter suggests that accessing TLS frequently within tight loops can indeed be a performance bottleneck, echoing the article's findings. Another comment highlights the overhead associated with the initial access to a TLS variable in a thread's lifetime, as opposed to subsequent accesses.
Finally, a few comments provide alternative solutions or approaches to consider when dealing with performance-sensitive multithreaded code. One commenter mentions using thread pools to minimize the overhead of thread creation and destruction, thus indirectly reducing the impact of TLS management. Another commenter suggests exploring alternative data structures or algorithms that might minimize the need for frequent TLS access altogether.